1. Technical Field of the Invention
This invention is related to a method for segmenting the color regions in an image for use in object-based image retrieval. More specifically, the invention partitions a multi-dimensional image into color regions, and color and border analysis is performed to segment the multi-dimensional image, or is performed to segment the multi-dimensional image into color regions.
2. Description of the Related Art
The following papers provide useful background information on the indicated topics, all of which relate to the invention, and are incorporated herein by reference:    J. Shi and J. Malik, Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), August 2000, pp. 888-905;    W. Y. Ma and B. S. Manjunath, Edge Flow: A Framework of Boundary Detection and Image Segmentation, Proc. IEEE International Conference on Computer Vision and Pattern Recognition, June 1997, pp. 744-49;    J. Z. Wang et al., Unsupervised Multiresolution Segmentation of Images with Low Depth of Field, IEEE Trans. On Pattern Analysis and Machine Intelligence, Cot. 23, No. 1, January 2001, pp. 85-90;    K. Hirata, S. Mukherjea, W. S. Li and Y. Hara, Integrating Image Matching and Classification for Multimedia Retrieval on the Web, IEEE International Conference on Multimedia Computing and Systems, June 1999;    K. Hirata, S. Mukherjea, W. S. Li, Y. Okamura and Y. Hara, Facilitating Object-based Navigation Through Multimedia Web Databases, Theory and Practice of Object Systems, Vol. 4(4), pp. 261-83 (1998).
There will now be provided a discussion of various topics to provide a proper foundation for understanding the invention.
Image retrieval is one of the key topics for the broadband Internet world. Computers store large amounts of video data and photographic data as stored image data. However, the stored image data is usually stored with no keywords or very few keywords. In addition, the stored image data is usually not categorized properly, since most users do not want to spend time classifying the stored image data or assign proper keywords to the stored image data.
In many cases, users want to use the objects contained in the stored image as the subject of a query. For example, a user may try to retrieve certain images that comprise the stored image data, i.e., “find all the pictures of my son on the beach.” In this query, the location and size of the object in the stored image data is not important. All the stored images that include the user's son have to be retrieved. This kind of search is not achieved just by evaluating the similarity of the whole image. The system has to extract the requested objects from the image and evaluate the similarity using the object as a unit. For effective image retrieval, image segmentation and attribute assignment have to be designed for image matching purpose.
Segmentation is an operation of paramount importance in a number of image processing applications. Several techniques have been proposed. Malik et al. integrate the texture and contour cues and partition the image into the regions based on the normalized cut algorithms. W. Y. Ma et al. detect the boundary based on the edge flow. Ma et al. also integrate intensity/color and texture discontinuities and segment the image. These algorithms work to detect the boundary from the image, which includes both textured and non-textured area. However, they are not designed to segment images for object-based image retrieval. For example, although the shape of each object and localization have been taken into account when segmenting the image, it is not clearly considered in the indexing phase.
In partitioning images, J. Wang et al. apply an algorithm that uses a low depth of field. This algorithm works well to extract a clear foreground object. However, it is sometimes difficult to determine if the objects are placed in the foreground or the background of the stored image.